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Journal articles on the topic 'Spatial Data'

To see the other types of publications on this topic, follow the link: Spatial Data.

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Ivanov, Sabin. "SPATIAL DATA MODELS." Journal Scientific and Applied Research 20, no. 1 (December 1, 2020): 40–46. http://dx.doi.org/10.46687/jsar.v20i1.303.

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Spatial data represents the shape, location, and spatial relationships of geographic features to other features. The form represents the geometry of the objects, the location is described by a list of x, y coordinates of discrete points of the objects, and the spatial connections (topological information) of the geographical objects determine the interaction between them. Spatial (coordinate) information can also include time-related data.

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2

Osborn, Wendy. "Unbounded Spatial Data Stream Query Processing using Spatial Semijoins." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 02 (March 1, 2021): 33–41. http://dx.doi.org/10.5383/juspn.15.02.005.

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In this paper, the problem of query processing in spatial data streams is explored, with a focus on the spatial join operation. Although the spatial join has been utilized in many proposed centralized and distributed query processing strategies, for its application to spatial data streams the spatial join operation has received very little attention. One identified limitation with existing strategies is that a bounded region of space (i.e., spatial extent) from which the spatial objects are generated needs to be known in advance. However, this information may not be available. Therefore, two strategies for spatial data stream join processing are proposed where the spatial extent of the spatial object stream is not required to be known in advance. Both strategies estimate the common region that is shared by two or more spatial data streams in order to process the spatial join. An evaluation of both strategies includes a comparison with a recently proposed approach in which the spatial extent of the data set is known. Experimental results show that one of the strategies performs very well at estimating the common region of space using only incoming objects on the spatial data streams. Other limitations of this work are also identified.

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3

Barkworth, M. E., and J. Mcgrew. "Combining herbarium data with spatial data: potential benefits, new needs." Czech Journal of Genetics and Plant Breeding 41, Special Issue (July 31, 2012): 59–64. http://dx.doi.org/10.17221/6136-cjgpb.

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4

Lee. "A study on the Spatial Sampling Method to Minimize Spatial Autocorrelation of Spatial and Geographical Data." Journal of the Korean Society of Civil Engineers 34, no. 4 (2014): 1317. http://dx.doi.org/10.12652/ksce.2014.34.4.1317.

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5

Wiemann, Stefan, and Lars Bernard. "Spatial data fusion in Spatial Data Infrastructures using Linked Data." International Journal of Geographical Information Science 30, no. 4 (September 24, 2015): 613–36. http://dx.doi.org/10.1080/13658816.2015.1084420.

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6

Kovaříček, P., and J. Hůla. "Field capacity determination from GPS spatial data." Research in Agricultural Engineering 49, No. 3 (February 8, 2012): 75–79. http://dx.doi.org/10.17221/4955-rae.

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For agricultural machinery management the actually reached machines capacity has a considerable importance. The data recorded by GPS monitoring enable to correct machines work productivity under concrete operational conditions. Assessment of machine aggregates operation records has proved effect of the operational factors onto operational efficiency reached on particular plots. The theoretical efficiency given by exploitation characteristics of machines has decreased effect of higher share of non-productive travels within small and irregular plots almost by 25%. In this paper we are dealing with searching for correlation between field speed and travelled unit path and defined classes of size, length and plot shape. The resulting knowledge of field efficiency on plots properties will enable to make more accurate the machines planned operation.

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7

Klimešová, D., and E. Ocelíková. "Spatial data modelling and maximum entropy theory." Agricultural Economics (Zemědělská ekonomika) 51, No. 2 (February 20, 2012): 80–83. http://dx.doi.org/10.17221/5080-agricecon.

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Spatial data modelling and consequential error estimation of the distribution function are key points of spatial analysis. For many practical problems, it is impossible to hypothesize distribution function firstly and some distribution models, such as Gaussian distribution, may not suit to complicated distribution in practice. The paper shows the possibility of the approach based on the maximum entropy theory that can optimally describe the spatial data distribution and gives  the actual error estimation. 

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8

K, Sivakumar. "Spatial Data Mining: Recent Trends in the Era of Big Data." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 912–16. http://dx.doi.org/10.5373/jardcs/v12sp7/20202182.

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9

USUI, Teruko. "Spatial Data Transfer Standard (SDTS) and Spatial Data Model." Theory and Applications of GIS 2, no. 1 (1994): 1–8. http://dx.doi.org/10.5638/thagis.2.1.

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10

Wang, Ting. "Adaptive Tessellation Mapping (ATM) for Spatial Data Mining." International Journal of Machine Learning and Computing 4, no. 6 (2015): 478–82. http://dx.doi.org/10.7763/ijmlc.2014.v6.458.

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11

Shuler, John A., and Nancy J. Obermeyer. "Spatial data and data centers." Journal of Academic Librarianship 27, no. 5 (September 2001): 391–93. http://dx.doi.org/10.1016/s0099-1333(01)00254-3.

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12

Wang, Shuliang, and Hanning Yuan. "Spatial Data Mining." International Journal of Data Warehousing and Mining 10, no. 4 (October 2014): 50–70. http://dx.doi.org/10.4018/ijdwm.2014100103.

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Big data brings the opportunities and challenges into spatial data mining. In this paper, spatial big data mining is presented under the characteristics of geomatics and big data. First, spatial big data attracts much attention from the academic community, business industry, and administrative governments, for it is playing a primary role in addressing social, economic, and environmental issues of pressing importance. Second, humanity is submerged by spatial big data, such as much garbage, heavy pollution and its difficulties in utilization. Third, the value in spatial big data is dissected. As one of the fundamental resources, it may help people to recognize the world with population instead of sample, along with the potential effectiveness. Finally, knowledge discovery from spatial big data refers to the basic technologies to realize the value of big data, and relocate data assets. And the uncovered knowledge may be further transformed into data intelligences.

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13

Bacao, Fernando, Maribel Yasmina Santos, and Martin Behnisch. "Spatial Data Science." ISPRS International Journal of Geo-Information 9, no. 7 (July 8, 2020): 428. http://dx.doi.org/10.3390/ijgi9070428.

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14

Vusovic, Nenad, Igor Svrkota, and Daniel Krzanovic. "Spatial data infrastructure." Mining and Metallurgy Engineering Bor, no. 3 (2013): 159–74. http://dx.doi.org/10.5937/mmeb1303159v.

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15

Banerjee, Sudipto. "Spatial Data Analysis." Annual Review of Public Health 37, no. 1 (March 18, 2016): 47–60. http://dx.doi.org/10.1146/annurev-publhealth-032315-021711.

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16

Stewart Fotheringham, A. "Analysing spatial data." Journal of Biogeography 32, no. 12 (December 2005): 2190. http://dx.doi.org/10.1111/j.1365-2699.2005.01364.x.

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17

Odeh, Inakwu O. A. "Spatial Data Quality." Geoderma 116, no. 3-4 (October 2003): 395–98. http://dx.doi.org/10.1016/s0016-7061(03)00115-0.

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18

Mamoulis, Nikos. "Spatial Data Management." Synthesis Lectures on Data Management 3, no. 6 (November 30, 2011): 1–149. http://dx.doi.org/10.2200/s00394ed1v01y201111dtm021.

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19

Nikparvar, Behnam, and Jean-Claude Thill. "Machine Learning of Spatial Data." ISPRS International Journal of Geo-Information 10, no. 9 (September 12, 2021): 600. http://dx.doi.org/10.3390/ijgi10090600.

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Properties of spatially explicit data are often ignored or inadequately handled in machine learning for spatial domains of application. At the same time, resources that would identify these properties and investigate their influence and methods to handle them in machine learning applications are lagging behind. In this survey of the literature, we seek to identify and discuss spatial properties of data that influence the performance of machine learning. We review some of the best practices in handling such properties in spatial domains and discuss their advantages and disadvantages. We recognize two broad strands in this literature. In the first, the properties of spatial data are developed in the spatial observation matrix without amending the substance of the learning algorithm; in the other, spatial data properties are handled in the learning algorithm itself. While the latter have been far less explored, we argue that they offer the most promising prospects for the future of spatial machine learning.

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20

Yu, J., L. Wu, Y. Yang, X. Lei, and W. He. "Global Data Spatially Interrelate System for Scientific Big Data Spatial-Seamless Sharing." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-6 (April 23, 2014): 125–30. http://dx.doi.org/10.5194/isprsarchives-xl-6-125-2014.

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A good data sharing system with spatial-seamless services will prevent the scientists from tedious, boring, and time consuming work of spatial transformation, and hence encourage the usage of the scientific data, and increase the scientific innovation. Having been adopted as the framework of Earth datasets by Group on Earth Observation (GEO), Earth System Spatial Grid (ESSG) is potential to be the spatial reference of the Earth datasets. Based on the implementation of ESSG, SDOG-ESSG, a data sharing system named global data spatially interrelate system (GASE) was design to make the data sharing spatial-seamless. The architecture of GASE was introduced. The implementation of the two key components, V-Pools, and interrelating engine, and the prototype is presented. Any dataset is firstly resampled into SDOG-ESSG, and is divided into small blocks, and then are mapped into hierarchical system of the distributed file system in V-Pools, which together makes the data serving at a uniform spatial reference and at a high efficiency. Besides, the datasets from different data centres are interrelated by the interrelating engine at the uniform spatial reference of SDOGESSG, which enables the system to sharing the open datasets in the internet spatial-seamless.

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21

Tao, Ran, and Jean-Claude Thill. "Spatial Cluster Detection in Spatial Flow Data." Geographical Analysis 48, no. 4 (April 28, 2016): 355–72. http://dx.doi.org/10.1111/gean.12100.

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22

Radosevic, Nenad, Matt Duckham, Mohammad Saiedur Rahaman, Serene Ho, Katherine Williams, Tanzima Hashem, and Yaguang Tao. "Spatial data trusts: an emerging governance framework for sharing spatial data." International Journal of Digital Earth 16, no. 1 (May 1, 2023): 1607–39. http://dx.doi.org/10.1080/17538947.2023.2200042.

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23

Fatchurohman, Hendy, Like Indrawati, Iswanti Rahayu Ningtiyas, Nur Anisa Nadhira, and Sasvita Gevi Meliyasari. "Data Pesawat Udara Tanpa Awak Untuk Pendukung Analisis Dinamika Pesisir dan Erosi Pantai." Jurnal Spatial Wahana Komunikasi dan Informasi Geografi 23, no. 2 (August 18, 2023): 29–40. http://dx.doi.org/10.21009/spatial.232.04.

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Kawasan pesisir menjadi salah satu area yang paling rentan terhadap ancaman bencana akibat percepatan perubahan iklim. Salah satu ancaman yang hampir terjadi di seluruh Kawasan pesisir di dunia adalah erosi pantai. Kawasan Pesisir Kabupaten Bantul, DIY menjadi salah satu area yang terdampak cukup parah oleh erosi pantai. Penelitian ini bertujuan untuk (1) Mengetahui proses dinamika pesisir di Kawasan Pantai Pandansimo ; (2) Memetakan tingkat kecepatan abrasi di Pantai Pandansimo, dan (3) Mengetahui penyebab utama dari proses erosi pantai yang terjadi. Citra satelit resolusi tinggi Pleiades dengan resolusi spasial 50cm digunakan untuk mengetahui perubahan garis pantasi secara temporal. Selain itu, citra satelit yang diperoleh dari Google Earth juga digunakan untuk analisis dari tahun 2010-2021. Perubahan garis pantai dihitung menggunakan software DSAS. Data morfologi detail didapatkan dari hasil akuisisi foto udara menggunakan pesawat tanpa awak multirotor. Berdasarkan hasil analisi diketahui bahwa proses dinamika pesisir didominasi oleh proses erosi pantai. Tercatat nilai kemunduran garis pantai rerata sebesar 2.46 meter/tahun. Erosi pantai yang terjadi disebabkan oleh beberapa faktor hidrodinamik dan morfodinamik seperti peningkatan kejadian gelombang ekstrem, berkurangnya suplai sedimen fluvial, dan fenomena perubahan iklim.

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24

Romano, Elvira, Antonio Balzanella, and Rosanna Verde. "Spatial variability clustering for spatially dependent functional data." Statistics and Computing 27, no. 3 (March 26, 2016): 645–58. http://dx.doi.org/10.1007/s11222-016-9645-2.

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25

Smith, N. S. "Spatial data models and data structures." Computer-Aided Design 22, no. 3 (April 1990): 184–90. http://dx.doi.org/10.1016/0010-4485(90)90077-p.

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26

Astuty, Yulia Indri, and Adi Wibowo. "ANALISIS SPASIAL TEMPORAL PERUBAHAN TUTUPAN LAHAN DI SEKITAR WADUK PENDIDIKAN DIPONEGORO MENGGUNAKAN DATA GOOGLE EARTH." Jurnal Spatial Wahana Komunikasi dan Informasi Geografi 23, no. 2 (August 31, 2023): 41–49. http://dx.doi.org/10.21009/spatial.232.05.

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Waduk Pendidikan Diponegoro terletak di Kecamatan Tembalang Kota Semarang Provinsi Jawa Tengah. Waduk ini dibangun untuk keperluan akademik dan juga sebagai upaya dalam pengendalian banjir. Kota Semarang memiliki tingkat kerawanan banjir yang cukup tinggi. Lokasi yang berbatasan dengan Laut Jawa diiringi dengan keterbatasan lahan dan peningkatan jumlah penduduk yang signifikan menjadi alasan klasik kota ini rawan banjir. Berdasarkan data Badan Pusat Statistik, tahun 2007 jumlah penduduk di Kota Semarang berkisar 1,45 juta jiwa, tahun 2015 naik menjadi 1,59 juta jiwa, dan pada tahun 2021 kembali meningkat hingga 1,65 juta jiwa. Peningkatan jumlah penduduk ini dapat menimbulkan berbagai masalah keruangan yang berdampak pada kerawanan bencana banjir, salah satunya adalah permasalahan penggunaan lahan. Oleh karena itu, dibutuhkan suatu analisis tutupan lahan di sekitar Waduk Pendidikan Diponegoro. Penelitian ini melakukan analisis spasial temporal tutupan lahan tahun 2007, 2015 dan 2022 dengan menggunakan data Googe Earth. Metode yang digunakan adalah digitasi on screen untuk mendapatkan 3 kelas klasifikasi tutupan lahan yaitu badan air, lahan terbangun dan non lahan terbangun. Harapannya adalah hasil dari penelitian ini dapaat menyajikan data tutupan lahan di sekitar Waduk Pendidikan Diponegoro tahun 2007, 2015 dan 2022 sebagai masukan kepada pemangku kebijakan terkait tata ruang dan kebencanaan untuk pembangunan berkelanjutan.

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27

Varadharajulu, P., M. Azeem Saqiq, F. Yu, D. A. McMeekin, G. West, L. Arnold, and S. Moncrieff. "SPATIAL DATA SUPPLY CHAINS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4/W7 (June 30, 2015): 41–45. http://dx.doi.org/10.5194/isprsarchives-xl-4-w7-41-2015.

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This paper describes current research into the supply of spatial data to the end user in as close to real time as possible via the World Wide Web. The Spatial Data Infrastructure paradigm has been discussed since the early 1990s. The concept has evolved significantly since then but has almost always examined data from the perspective of the supplier. It has been a supplier driven focus rather than a user driven focus. The current research being conducted is making a paradigm shift and looking at the supply of spatial data as a supply chain, similar to a manufacturing supply chain in which users play a significant part. A comprehensive consultation process took place within Australia and New Zealand incorporating a large number of stakeholders. Three research projects that have arisen from this consultation process are examining Spatial Data Supply Chains within Australia and New Zealand and are discussed within this paper.

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28

de Veaux, Richard D., and Noel A. C. Cressie. "Statistics for Spatial Data." Technometrics 35, no. 3 (August 1993): 322. http://dx.doi.org/10.2307/1269525.

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29

Ziegel, Eric R., and Noel Cressie. "Statistics for Spatial Data." Technometrics 36, no. 4 (November 1994): 437. http://dx.doi.org/10.2307/1269985.

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30

Palmer, Michael W., Trevor C. Bailey, and Anthony C. Gatrell. "Interactive Spatial Data Analysis." Ecology 77, no. 5 (July 1996): 1642. http://dx.doi.org/10.2307/2265559.

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31

Teng, Haotian, Ye Yuan, and Ziv Bar-Joseph. "Clustering spatial transcriptomics data." Bioinformatics 38, no. 4 (October 8, 2021): 997–1004. http://dx.doi.org/10.1093/bioinformatics/btab704.

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Abstract Motivation Recent advancements in fluorescence in situ hybridization (FISH) techniques enable them to concurrently obtain information on the location and gene expression of single cells. A key question in the initial analysis of such spatial transcriptomics data is the assignment of cell types. To date, most studies used methods that only rely on the expression levels of the genes in each cell for such assignments. To fully utilize the data and to improve the ability to identify novel sub-types, we developed a new method, FICT, which combines both expression and neighborhood information when assigning cell types. Results FICT optimizes a probabilistic function that we formalize and for which we provide learning and inference algorithms. We used FICT to analyze both simulated and several real spatial transcriptomics data. As we show, FICT can accurately identify cell types and sub-types, improving on expression only methods and other methods proposed for clustering spatial transcriptomics data. Some of the spatial sub-types identified by FICT provide novel hypotheses about the new functions for excitatory and inhibitory neurons. Availability and implementation FICT is available at: https://github.com/haotianteng/FICT. Supplementary information Supplementary data are available at Bioinformatics online.

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32

Clayton, Murray K., and Noel Cressie. "Statistics for Spatial Data." Journal of the American Statistical Association 88, no. 422 (June 1993): 703. http://dx.doi.org/10.2307/2290365.

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33

Cormack, R. M., and N. Cressie. "Statistics for Spatial Data." Biometrics 48, no. 4 (December 1992): 1300. http://dx.doi.org/10.2307/2532724.

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34

Phillips, Andrew, Ian Williamson, and Chukwudozie Ezigbalike. "Spatial Data Infrastructure Concepts." Australian Surveyor 44, no. 1 (June 1999): 20–28. http://dx.doi.org/10.1080/00050351.1999.10558768.

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35

Gotway, Carol A., and Linda J. Young. "Combining Incompatible Spatial Data." Journal of the American Statistical Association 97, no. 458 (June 2002): 632–48. http://dx.doi.org/10.1198/016214502760047140.

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36

Sha, Ziyuan, Xudong Zhu, Peiyao Zhao, and Zhaocheng Wang. "Data-Aided Spatial Modulation." IEEE Access 5 (2017): 7285–93. http://dx.doi.org/10.1109/access.2017.2696575.

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37

Haining, R., S. Wise, and J. Ma. "Exploratory Spatial Data Analysis." Journal of the Royal Statistical Society: Series D (The Statistician) 47, no. 3 (September 1998): 457–69. http://dx.doi.org/10.1111/1467-9884.00147.

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38

Agterberg, Frederik P. "Interactive spatial data analysis." Computers & Geosciences 22, no. 8 (October 1996): 953–54. http://dx.doi.org/10.1016/s0098-3004(96)80468-7.

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39

Veaux, Richard D. De. "Statistics for Spatial Data." Technometrics 35, no. 3 (August 1993): 321–23. http://dx.doi.org/10.1080/00401706.1993.10485328.

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40

Tilke, Clemens. "Statistics for spatial data." Computational Statistics & Data Analysis 14, no. 4 (November 1992): 547. http://dx.doi.org/10.1016/0167-9473(92)90071-m.

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41

Smith, M. J. "Spatial data 2000 conference." ISPRS Journal of Photogrammetry and Remote Sensing 47, no. 1 (February 1992): 71–72. http://dx.doi.org/10.1016/0924-2716(92)90010-7.

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42

Lawson, A., and N. Cressie. "Statistics for Spatial Data." Statistician 42, no. 1 (1993): 73. http://dx.doi.org/10.2307/2348117.

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43

Haslett, John. "Spatial Data Analysis-Challenges." Statistician 41, no. 3 (1992): 271. http://dx.doi.org/10.2307/2348549.

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44

Hunter, Gary J., Monica Wachowicz, and Arnold K. Bregt. "Understanding Spatial Data Usability." Data Science Journal 2 (2003): 79–89. http://dx.doi.org/10.2481/dsj.2.79.

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45

Ilic, Aleksandar. "Global spatial data infrastructure." Journal of the Geographical Institute Jovan Cviji?, SASA 59, no. 1 (2009): 179–94. http://dx.doi.org/10.2298/ijgi0959179i.

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46

Rees, Mike, and N. Cressie. "Statistics for Spatial Data." Journal of the Royal Statistical Society. Series A (Statistics in Society) 156, no. 1 (1993): 126. http://dx.doi.org/10.2307/2982871.

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47

Love, A. L., A. Pang, and D. L. Kao. "Visualizing Spatial Multivalue Data." IEEE Computer Graphics and Applications 25, no. 3 (May 2005): 69–79. http://dx.doi.org/10.1109/mcg.2005.71.

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48

Cressie, Noel. "STATISTICS FOR SPATIAL DATA." Terra Nova 4, no. 5 (September 1992): 613–17. http://dx.doi.org/10.1111/j.1365-3121.1992.tb00605.x.

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49

Haining, Robert. "Statistics for spatial data." Computers & Geosciences 19, no. 4 (April 1993): 615–16. http://dx.doi.org/10.1016/0098-3004(93)90088-m.

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50

PENG, Nan-feng. "Volume rendering of spatial scattered data based on spatial data interpolation algorithms." Journal of Computer Applications 28, no. 7 (November 3, 2008): 1759–60. http://dx.doi.org/10.3724/sp.j.1087.2008.01759.

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